Duangsuwan, Sarun

Preferred name

Duangsuwan, Sarun

Alternative Name

Duangsuwan, S.

Main Affiliation

KMITL Prince of Chumphon Campus

sarun.du@kmitl.ac.th

Now showing 1 - 10 of 18

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Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment
(2021-02-01)
Duangsuwan, Sarun
;
Maw, Myo Myint
The comparison of path loss model for the unmanned aerial vehicle (UAV) and Internet of Things (IoT) air-to-ground communication system was proposed for rural precision farming. Due to the uncertainty of propagation channel in rural precision farming environment, the comparison of path loss prediction was investigated by the conventional particle swarm optimization (PSO) algorithms: PSO (exponential or Exp), PSO (polynomial or Poly) and the machine learning algorithms: k-nearest neighbor (k-NN), and random forest, are exploited to accurate the path loss models on the basic of the measured dataset. Meanwhile, the empirical model in the rural precision farming was considered. By using the machine learning-based algorithms, the coefficient of determination (R-squared: R<sup>2</sup>) and root mean squared error (RMSE) were evaluated as highly accuracy and precision more than the conventional PSO algorithms. According to the results, the random forest method was able to perform more than other methods. It has the smallest prediction errors.
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Underwater Drone-Enabled Wireless Communication Systems for Smart Marine Communications: A Study of Enabling Technologies, Opportunities, and Challenges
(2025-11-01)
Duangsuwan, Sarun
;
Klubsuwan, Katanyoo
Highlights: What are the main findings? This paper reviews underwater wireless communication methods, including acoustic, optical, and RF communication, in marine applications, and explores the potential of existing underwater drones. This paper examines the opportunities and challenges of hybrid wireless communication systems for underwater drones. What is the implication of the main finding? This paper considers the integration of underwater drones, IoUT, AI-driven data, VR, and DT for smart marine communications. Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem.
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A WiFi Link Budget Analysis of Drone-based Communication and IoT Ground Sensors
(2021-04-01)
Supramongkonset, Jatupotn
;
Duangsuwan, Sarun
;
Promwong, Sathapotn
An application of drone-based wireless networking in agricultural scenarios needs to analyze the radio link budget because of the uncertain propagation from the surrounding. This paper presents a link budget analysis between drone as a drone small cell (DSC) and Internet of Things (IoT) as a ground sensor. The measurement results show the received signal strength indicator (RSSI), path loss, and delay spread at 2.4 GHz frequency when considering the numbers of ground sensors to 15 points and use a single drone enabled with WiFi portable link. It can be found that drone DSC can compensate for the limited power of ground sensors and link budget analysis can optimally evaluate the communication channel in this scenario.
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Identifying Geomagnetic Storms with Ionospheric Storm Scale for GNSS and Disaster Prevention
(2020-03-01)
Jamjareegulgarn, Punyawi
;
Duangsuwan, Sarun
;
Supnithi, Pornchai
;
Budtho, Jirapoom
;
Tangtrakunphaisan, Udomsit
This paper proposes an ionospheric storm scale (I-scale) for identifying the impact of geomagnetic or ionospheric storms in the Ionosphere for GNSS (global navigation satellite system) service and disaster prevention. The I-scale in this work is computed based on the observed foF2 at Chumphon station (10.72°N, 99.37°E) over equatorial latitude from January 2004 to July 2018. The results report that the severe geomagnetic storms, i.e., IP3 and IN3, seldom occur at Chumphon with the probabilities of 0.02% and 0.07%, respectively. The probability of quiet ionospheric condition is the maximum value of 70.73%. Meanwhile, the other I-scales sometimes occur and range from 0.60% to 13.97%. The benefits of the foF2-based I-scale are to indicate the violence level of geomagnetic storms and to announce the ionospheric irregularities in practice.
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Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios
(2021-01-01)
Duangsuwan, Sarun
;
Juengkittikul, Phakamon
;
Myint Maw, Myo
The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.
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Empirical Path Loss Channel Characterization Based on Air-to-Air Ground Reflection Channel Modeling for UAV-Enabled Wireless Communications
(2021-01-01)
Supramongkonset, Jatuporn
;
Duangsuwan, Sarun
;
Maw, Myo Myint
;
Promwong, Sathaporn
The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.
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Development of drone real-time air pollution monitoring for mobile smart sensing in areas with poor accessibility
(2020-01-01)
Duangsuwan, Sarun
;
Jamjareekulgarn, Punyawi
The topic of air pollution, especially in terms of particulate matter (PM), is a very serious problem in current society. This problem is caused by such factors as forest fires, construction, industrialization, and the ever-increasing number of motor vehicles. Thus, PM2.5 has become an important risk factor for citizens in Thailand as well as globally, not only in terms of the problems associated with health risks, but also the negative impact on the image of the country. Measuring pollution for air quality monitoring is a challenging task, especially when considering areas that have poor accessibility. The aim of this work is to develop a drone equipped with sensors to monitor and collect air quality data in real time from such areas of potential pollution. The proposed drone is called the drone for real-time air pollution monitoring (Dr-TAPM) and is equipped with the ability to measure the concentration of carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), PM, and sulfur dioxide (SO<inf>2</inf>). Additionally, the collected data is transmitted to a cloud server every second over a wireless internet connection. In this study, the measurement was conducted in the experiment area, which is considered to be in the pollutant model scenario. The experimental results are shown as graphs of quantitative pollutant levels and air quality index (AQI) values obtained from realtime monitoring on a mobile application.
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A Study of A2A Channel Modeling for Small UAV-Enabled Wireless Communication
(2022-01-01)
Supramongkonset, Jatuporn
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Duangsuwan, Sarun
;
Promwong, Sathaporn
The objective of this work is to study the ground reflection modeling case study of air-to-air (A2A) channel modeling for an unmanned aerial vehicle (UAV)-enabled wireless communications. We consider the Internet of Things (IoT) WiFi modules at 2.4 GHz standard enabled with the transmitter Tx- UAV and the receiver Rx-UAV. The received signal strength indicator (RSSI) and path loss characteristics are addressed by using the free space path loss (FSPL) model, air-to-air two- ray (A2AT-R) model, and the modified Log-distance path loss model. The measurement results indicated that the limit of power constraint of WiFi modules was a level at 10 m Rx-UAV altitude, and the received sensitivity was limited at -95 dBm. It is shown that the impact of ground reflection path loss was characterized at 1-3 m Rx-Uavaltitudes which are accordingly related to the A2AT-R model and the relative resulting with the modified Log- distance model above 4 m Rx-UAV altitudes.
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Drone-Enabled AI Edge Computing and 5G Communication Network for Real-Time Coastal Litter Detection
(2024-12-01)
Duangsuwan, Sarun
;
Prapruetdee, Phoowadon
Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and other plastics can take hundreds of years to degrade, threatening marine life through ingestion, entanglement, and habitat destruction. To address this issue, we deploy drones equipped with high-resolution cameras and sensors to capture detailed coastal imagery for assessing litter distribution. This study presents the development of an AI-driven coastal litter detection system using edge computing and 5G communication networks. The AI edge server utilizes YOLOv8 and a recurrent neural network (RNN) to enable the drone to detect and classify various types of litter, such as bottles, cans, and plastics, in real-time. High-speed 5G communication supports seamless data transmission, allowing efficient monitoring. We evaluated drone performance under optimal flying heights above ground of 5 m, 7 m, and 10 m, analyzing accuracy, precision, recall, and F1-score. Results indicate that the system achieves optimal detection at an altitude of 5 m with a ground sampling distance (GSD) of 0.98 cm/pixel, yielding an F1-score of 98% for cans, 96% for plastics, and 95% for bottles. This approach facilitates real-time monitoring of coastal areas, contributing to marine ecosystem conservation and environmental sustainability.
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Performance Analysis of Unmanned Aerial Vehicle Assisted Wireless IoT Sensors Based on Air-to-Ground Communication Model for Smart Farming
(2023-01-01)
Duangsuwan, Sarun
;
Promwong, Sathaporn
We used an unmanned aerial vehicle (UAV) and IoT as a new platform for soil moisture monitoring based on the air-to-ground (A2G) communication model. We investigated an energyefficient UAV trajectory by considering the power outage probability and transmission rate for UAV-assisted wireless IoT sensor connectivity. We considered the closed-form power outage probability for IoT sensors located within the coverage zone of a UAV drone small cell. We conducted experiments in the Napier and Ruzi grass farms with IoT sensors for detecting soil moisture along a drip line irrigation system located in the fields. The power outage probability is given for different UAV heights and transmission rates, which contributes to reliable communication with IoT sensors.

Duangsuwan, Sarun

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